//! Lenia Dynamics Engine — zero-copy operations on tensor memory.
//!
//! Python passes numpy arrays (which share memory with PyTorch tensors).
//! Rust operates on the underlying f32 data directly. No copies.
//! Results are written back to the same memory.
//!
//! The hot path per weight matrix:
//!   1. Convolve with ring kernel → neighborhood potential
//!   2. Growth function → bell curve centered on target potential
//!   3. Modulate by activation magnitude
//!   4. Compute + clamp delta
//!   5. Apply delta IN PLACE
//!   6. Clip to bounds
//!   7. Mass conservation (L1 norm preservation)

use pyo3::prelude::*;
use numpy::{PyArray1, PyReadonlyArray1, PyArrayMethods};
use crate::kernel::Kernel2D;
use std::time::Instant;

/// Result from a full Lenia step across all matrices.
#[pyclass]
#[derive(Clone)]
pub struct LeniaStepResult {
    #[pyo3(get)]
    pub total_delta_norm: f64,
    #[pyo3(get)]
    pub matrices_processed: usize,
    #[pyo3(get)]
    pub matrices_skipped: usize,
    #[pyo3(get)]
    pub time_ms: f64,
    #[pyo3(get)]
    pub step_count: u64,
}

/// The Lenia dynamics engine. Operates directly on numpy array memory.
#[pyclass]
pub struct RustLeniaEngine {
    kernel: Kernel2D,
    growth_mu: f32,
    growth_sigma: f32,
    growth_scale: f32,
    max_weight_delta: f32,
    weight_clip_min: f32,
    weight_clip_max: f32,
    activation_coupling: f32,
    step_count: u64,
    total_time_ms: f64,
    initial_norms: Vec<f64>,
    /// Reusable scratch buffer for convolution output
    scratch: Vec<f32>,
}

#[pymethods]
impl RustLeniaEngine {
    #[new]
    #[pyo3(signature = (
        kernel_radius = 5,
        kernel_sigma = 0.8,
        growth_mu = 0.12,
        growth_sigma = 0.02,
        growth_scale = 0.005,
        max_weight_delta = 0.05,
        weight_clip_min = -3.0,
        weight_clip_max = 3.0,
        activation_coupling = 2.0,
    ))]
    pub fn new(
        kernel_radius: usize,
        kernel_sigma: f32,
        growth_mu: f32,
        growth_sigma: f32,
        growth_scale: f32,
        max_weight_delta: f32,
        weight_clip_min: f32,
        weight_clip_max: f32,
        activation_coupling: f32,
    ) -> Self {
        RustLeniaEngine {
            kernel: Kernel2D::new(kernel_radius, kernel_sigma),
            growth_mu,
            growth_sigma,
            growth_scale,
            max_weight_delta,
            weight_clip_min,
            weight_clip_max,
            activation_coupling,
            step_count: 0,
            total_time_ms: 0.0,
            initial_norms: Vec::new(),
            scratch: Vec::new(),
        }
    }

    /// Process a single weight matrix IN PLACE.
    ///
    /// Args:
    ///   weights: numpy array (flattened f32) — MODIFIED IN PLACE
    ///   rows: matrix height
    ///   cols: matrix width
    ///   activation_mag: activation magnitude for this layer
    ///   matrix_idx: index for mass conservation tracking
    ///
    /// Returns delta_norm for this matrix.
    pub fn step_single_inplace(
        &mut self,
        py: Python<'_>,
        weights: &Bound<'_, PyArray1<f32>>,
        rows: usize,
        cols: usize,
        activation_mag: f32,
        matrix_idx: usize,
    ) -> PyResult<f64> {
        let n = rows * cols;
        let min_size = 2 * self.kernel.radius + 1;

        if rows < min_size || cols < min_size {
            return Ok(0.0);
        }

        // Get mutable access to the numpy array's data — zero copy
        let mut weights_rw = unsafe { weights.as_array_mut() };
        let w_slice = weights_rw.as_slice_mut()
            .ok_or_else(|| pyo3::exceptions::PyValueError::new_err("Array not contiguous"))?;

        // Initialize norm on first visit
        while self.initial_norms.len() <= matrix_idx {
            self.initial_norms.push(0.0);
        }
        if self.initial_norms[matrix_idx] == 0.0 {
            self.initial_norms[matrix_idx] = w_slice.iter().map(|v| v.abs() as f64).sum();
        }

        // Ensure scratch buffer is large enough
        if self.scratch.len() < n {
            self.scratch.resize(n, 0.0);
        }

        // 1. Convolve — neighborhood potential
        self.kernel.convolve(w_slice, rows, cols, &mut self.scratch[..n]);

        // 2-5. Growth + modulation + delta + apply — all in one pass
        let mu = self.growth_mu;
        let sigma = self.growth_sigma;
        let scale = self.growth_scale;
        let max_d = self.max_weight_delta;
        let clip_min = self.weight_clip_min;
        let clip_max = self.weight_clip_max;

        let act_scale = if self.activation_coupling > 0.0 && activation_mag > 0.0 {
            (activation_mag * self.activation_coupling).tanh()
        } else {
            1.0
        };

        let mut delta_sum = 0.0f64;

        for i in 0..n {
            let p = self.scratch[i];
            // Growth function: bell curve
            let g = 2.0 * (-(p - mu).powi(2) / (2.0 * sigma * sigma)).exp() - 1.0;
            // Modulate + scale + clamp
            let d = (scale * g * act_scale).clamp(-max_d, max_d);
            // Apply + clip
            w_slice[i] = (w_slice[i] + d).clamp(clip_min, clip_max);
            delta_sum += d.abs() as f64;
        }

        // 7. Mass conservation — preserve L1 norm
        let current_norm: f64 = w_slice.iter().map(|v| v.abs() as f64).sum();
        let target_norm = self.initial_norms[matrix_idx];

        if current_norm > 1e-10 {
            let factor = (target_norm / current_norm) as f32;
            for v in w_slice.iter_mut() {
                *v *= factor;
            }
        }

        Ok(delta_sum / n as f64)
    }

    /// Process all weight matrices in one call.
    ///
    /// Args:
    ///   weight_arrays: list of numpy arrays (each flattened, MODIFIED IN PLACE)
    ///   shapes: list of (rows, cols) tuples
    ///   activations: list of activation magnitudes
    ///
    /// Returns LeniaStepResult.
    pub fn step_all_inplace(
        &mut self,
        py: Python<'_>,
        weight_arrays: Vec<Bound<'_, PyArray1<f32>>>,
        shapes: Vec<(usize, usize)>,
        activations: Vec<f32>,
    ) -> PyResult<LeniaStepResult> {
        let start = Instant::now();
        let n = weight_arrays.len();
        let mut total_delta = 0.0f64;
        let mut processed = 0usize;
        let mut skipped = 0usize;

        for (i, arr) in weight_arrays.iter().enumerate() {
            let (rows, cols) = shapes[i];
            let act = if i < activations.len() { activations[i] } else { 0.0 };

            let delta = self.step_single_inplace(py, arr, rows, cols, act, i)?;
            if delta > 0.0 {
                total_delta += delta;
                processed += 1;
            } else {
                skipped += 1;
            }
        }

        let elapsed = start.elapsed().as_secs_f64() * 1000.0;
        self.step_count += 1;
        self.total_time_ms += elapsed;

        Ok(LeniaStepResult {
            total_delta_norm: total_delta,
            matrices_processed: processed,
            matrices_skipped: skipped,
            time_ms: elapsed,
            step_count: self.step_count,
        })
    }

    pub fn get_summary(&self) -> (u64, f64, f64) {
        let avg = if self.step_count > 0 {
            self.total_time_ms / self.step_count as f64
        } else {
            0.0
        };
        (self.step_count, self.total_time_ms, avg)
    }
}

#[cfg(test)]
mod tests {
    use super::*;

    #[test]
    fn test_growth_function_shape() {
        let mu = 0.12f32;
        let sigma = 0.02f32;
        let at_mu = 2.0 * (-(0.0f32).powi(2) / (2.0 * sigma * sigma)).exp() - 1.0;
        assert!((at_mu - 1.0).abs() < 0.001);

        let far = 2.0 * (-((1.0 - mu) / sigma).powi(2) / 2.0).exp() - 1.0;
        assert!(far < -0.9);
    }
}